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Category: LLMs

  • Understanding Transformer Models in LLMs

    Transformer Models in LLMs 1. Core Innovation: Self-Attention The Transformer model’s revolutionary aspect for Large Language Models (LLMs) and Natural Language Processing (NLP) lies in its ability to process sequential data efficiently and understand context effectively. Unlike sequential models like Recurrent Neural Networks (RNNs), Transformers can process entire sequences in parallel. The key to this Read more

  • Salesforce Agentic AI: A Comprehensive Overview

    Salesforce Agentic AI: A Comprehensive Overview Salesforce Agentic AI represents a significant evolution in how artificial intelligence is integrated into the Salesforce platform. Moving beyond simple automation and predictive analytics, Agentic AI aims to create intelligent, autonomous agents capable of understanding complex goals, planning multi-step actions, and executing tasks on behalf of users. This detailed Read more

  • AI Agent with Short-Term Memory on AWS

    AI Agent with Short-Term Memory on AWS In the realm of Artificial Intelligence, creating agents that can effectively interact with their environment and solve complex tasks often requires equipping them with a form of short-term memory, also known as “scratchpad” or working memory. This allows the agent to temporarily store and process information relevant to Read more

  • AI Agent with Scratchpad Memory on AWS

    AI Agents with Scratchpad Memory on AWS AI agents equipped with “scratchpad” memory, or short-term working memory, significantly enhance their capabilities by allowing them to temporarily store and process information relevant to their current tasks. This enables them to handle complex scenarios, maintain context across interactions, and reason more effectively. This article explores the use Read more

  • Implementing Graph-Based Retrieval Augmented Generation

    Implementing Graph-Based Retrieval Augmented Generation Implementing Graph-Based Retrieval Augmented Generation This document outlines the implementation of a system that combines the power of Large Language Models (LLMs) with structured knowledge from a graph database to perform advanced question answering. This approach, known as Graph-Based Retrieval Augmented Generation (RAG), allows us to answer complex queries that Read more

  • Detailed Implementation of Backend-Only Advanced RAG with Multi-Hop Retrieval

    Detailed Implementation of Backend-Only Advanced RAG with Multi-Hop Retrieval This article provides a comprehensive guide to implementing a backend-only Retrieval-Augmented Generation (RAG) system enhanced with Multi-Hop Retrieval capabilities. This advanced technique, leveraging LangChain’s SelfQueryRetriever, OpenAI’s language models and embeddings, and ChromaDB for vector storage, enables more sophisticated question answering over a knowledge base. Understanding Multi-Hop Read more

  • Backend-Only Advanced RAG with Multi-Step Self-Correction

    Backend-Only Advanced RAG with Multi-Step Self-Correction Backend-Only Advanced RAG with Multi-Step Self-Correction This HTML document describes a backend-only implementation of a Retrieval-Augmented Generation (RAG) system featuring an advanced Multi-Step Self-Correction mechanism using Python, LangChain, OpenAI, and ChromaDB. Overview The goal of this project is to demonstrate how to build a RAG pipeline where the language Read more

  • Intelligent Chatbot with RAG using React and Python

    Intelligent Chatbot with RAG using React and Python This guide will walk you through building an intelligent chatbot using React.js for the frontend and Python with Flask for the backend, enhanced with Retrieval-Augmented Generation (RAG). RAG allows the chatbot to ground its responses in external knowledge sources, leading to more accurate and contextually relevant answers. Read more

  • Comparing Top LLMs

    Comparing Top LLMs (April 2025) The landscape of Large Language Models (LLMs) is constantly evolving. Here’s a comparison of some of the top contenders as of late April 2025, keeping in mind that rankings & capabilities can shift rapidly: Top 8 LLMs (Based on Current Trends & Capabilities): GPT-4o (OpenAI): Known for its strong general Read more

  • Empowering RAG with Microservices

    Adding Power to RAG with Microservices Adding more power to Retrieval-Augmented Generation (RAG) through the strategic use of microservices can significantly enhance its capabilities, scalability, maintainability, and overall effectiveness. Here’s a breakdown of how microservices can be leveraged to augment RAG: Core RAG Workflow and Potential Microservice Breakdown: A typical RAG workflow involves these steps: Read more